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Related Experiment Video

Updated: Feb 5, 2026

Fabrication of Polymer Microspheres for Optical Resonator and Laser Applications
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New Image Texture Analysis, and Application to Polymer Membrane Surface Morphologies and Roughness.

Clifford S Todd1, William A Heeschen1

  • 1Analytical Science, Core R&D,The Dow Chemical Company,1897 Building,Midland, MI 48667,USA.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|September 21, 2018
PubMed
Summary
This summary is machine-generated.

A novel image texture analysis method uses gray level statistics and principal component analysis for comprehensive image assessment. This technique effectively characterizes surface morphology and predicts roughness in scanning electron microscope images.

Keywords:
SEMmembranesmorphologyprincipal componenttexture

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Area of Science:

  • Materials Science
  • Image Analysis
  • Surface Science

Background:

  • Image texture analysis is crucial for characterizing material surfaces.
  • Existing methods may be computationally intensive or limited in scope.
  • Scanning electron microscopy (SEM) provides high-resolution surface imaging.

Purpose of the Study:

  • To introduce a new, computationally efficient image texture analysis method.
  • To apply the method to SEM images of reverse osmosis membranes.
  • To correlate texture analysis results with surface morphology and roughness.

Main Methods:

  • Utilized mean and standard deviation of gray levels within image domains.
  • Employed recursive calculations on domains of varying sizes.
  • Input gray level data into principal component analysis (PCA).
  • Analyzed images holistically, not for segmentation, across all scales and directions.

Main Results:

  • The method successfully identified surface morphology in SEM images.
  • Texture analysis accurately predicted surface roughness, as validated by atomic force microscopy (AFM).
  • The technique demonstrated robustness across domain sizes from 23 nm to 3 µm.

Conclusions:

  • The proposed image texture analysis is a powerful tool for characterizing material surfaces.
  • It offers a computationally efficient and comprehensive approach to texture analysis.
  • The method shows promise for predicting surface properties like roughness from SEM data.